Explainable AI (XAI) is widely viewed as a sine qua non for ever-expanding AI research. A
better understanding of the needs of XAI users, as well as human-centered evaluations of …

Abstract Explainability in Artificial Intelligence (AI) has been revived as a topic of active
research by the need of conveying safety and trust to users in the “how” and “why” of …

Abstract Previous research in Explainable Artificial Intelligence (XAI) suggests that a main
aim of explainability approaches is to satisfy specific interests, goals, expectations, needs …

Abstract Algorithms based on Artificial Intelligence technologies are slowly transforming
street‐level bureaucracies, yet a lack of algorithmic transparency may jeopardize citizen …

With the recent advances in the field of artificial intelligence, an increasing number of
decision-making tasks are delegated to software systems. A key requirement for the success …

Transparency can empower users to make informed choices about how they use an
algorithmic decision-making system and judge its potential consequences. However …

Argumentation and eXplainable Artificial Intelligence (XAI) are closely related, as in the
recent years, Argumentation has been used for providing Explainability to AI. Argumentation …

Recommender Systems are nowadays successfully used by all major web sites—from e-
commerce to social media—to filter content and make suggestions in a personalized way …

Over the past two decades, a large amount of research effort has been devoted to
develo** algorithms that generate recommendations. The resulting research progress has …

The multi-armed bandit is an important framework for balancing exploration with exploitation
in recommendation. Exploitation recommends content (eg, products, movies, music playlists) …